DMIS LAB

Data Mining and Information Systems Lab

(고려대학교 컴퓨터학과 강재우교수 연구실)

About

Data science has advanced to the point where it is changing our world. It is now the center of exploring and uncovering knowledge in different domains and acts as a bridge to connect them. With ever growing amount of data and opportunity to explore, DMIS lab aims to drive the data science revolution.

DMIS (Data Mining and Information Systems) Lab seeks to develop explainable AI in the following areas: Drug Discovery, Bioinformatics Analysis, Biomedical Image Processing, Recommender Systems, Question and Answering, Search, Financial Data Analysis, and much more. We focus on finding models, algorithms, and systems for any kinds of data analysis with applications on prediction, knowledge discovery, representation learning and anomaly detection. DMIS Lab is also participating various data science competitions such as DREAM Challenges to solve difficult real-world problems and facilitate knowledge sharing with other research teams around the world.

July. 2019: Congratulations! Our DMIS team (Sungjoon Park, Minji Jeon, Sunkyu Kim, Junhyun Lee, Seongjun Yun, Bumsoo Kim, Buru Chang) has been selected as the top performers in the IDG-DREAM Drug-Kinase Binding Prediction Challenge. As one of the best performers, we will present our model at the RSG with DREAM Conference, NY in November. (Link)

May. 2019: ReSimNet: Drug Response Similarity Prediction using Siamese Neural Networks, co-first authored by Minji Jeon and Donghyeon Park, has been accepted to Bioinformatics, the best journal for computational biology.

ReSimNet measures the transcriptional response similarity of the two chemical compounds, and the team achieved first place in the Multi-targeting Drug DREAM Challenge with this model (outperforming Janssen Pharmaceutica).

Apr. 2019: Self-Attention Graph Pooling, co-first authored by Junhyun Lee and Inyeop Lee, has been accepted to ICML 2019, the top conference in machine learning.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen is an overview paper for the DREAM challenge and is coauthored by top performing teams and organizers from AstraZeneca-Sanger. (bioarxiv)

Dec. 2018: Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network, co-first authored by Raehyun Kim and Hyunjae Kim, has been accepted as full paper by SDM19, one of the top-tier conferences in data-mining.

Nov. 2018: Buru Chang received the NAVER Ph.D Fellowship Award as he showed stellar performance with his papers.

Aug. 2018: Jinhyuk Lee's paper, Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering, was accepted to EMNLP2018, one of the most renowned conferences in NLP field.

Aug. 2018: Learning User Preferences and Understanding Calendar Contexts for Event Scheduling (co-first authored by Donghyeon Kim and Jinhyuk Lee) got accepted by CIKM2018, which is one of the top-tier international conferences in Database/Data Mining/Information Retrieval field with 17% acceptance rate.

Jul. 2018: Buru Chang's paper, Content-Aware Point-of-Interest Embedding Model for Successive POI Recommendation, was accepted to IJCAI 2018, one of the top-tier conferences for general AI.

Nov. 2017: Our DMIS team (Sunkyu Kim, Heewon Lee, Keonwoo Kim, Hwisang Jeon, Minji Jeon, Yonghwa Choi, Daehan Kim) was awarded as the BEST performers of the NCI-CPTAC DREAM Proteogenomics Challenge, sponsored by the National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC). This was the very first time that Korea team won the Challenge. (Link)

Aug. 2017: Jinhyuk Lee's paper, Name Nationality Classification with Recurrent Neural Network, got accepted for IJCAI 2017, one of the top-tier conferences for general AI.

Apr. 2017: Constructing and Evaluating a Novel Crowdsourcing-based Paraphrased Opinion Spam Dataset, co-first authored by Seongsoon Kim and Seongwoon Lee, has been accepted to WWW 2017, one of the top conferences for web.

Oct. 2016: Among 42 teams from different parts of the world, our DMIS team ranked 2nd place at the Disease Module Identification DREAM Challenge: Discover disease pathways in genomic networks. The goal is to systematically assess module identification methods on a panel of state-of-the-art genomic networks and to discover novel network pathways.

Mar. 2016: Our DMIS team won 2nd place at the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is designed to predict synergistic drug combinations and to identify associated biomarkers. As the challenge was hosted by AstraZeneca, one of the top 10 pharmaceutical companies in the world, the DMIS team showed stellar performance in this grand competition, ranking 2nd place. (Link)